Journal article Open Access

A cloud based architecture capable of perceiving and predicting multiple vessel behaviour

Zissis, Dimitrios; Xidias, Elias K.; Lekkas, Dimitrios

Progressively huge amounts of data, tracking vessels during their voyages across the seas, are becoming available, mostly due to the Automatic Identification System (AIS) that vessels of specific categories are required to carry. These datasets provide detailed insights into the patterns vessels follow, while safely navigating across the globe, under various conditions. In this paper, we develop an Artificial Neural Network (ANN) capable of predicting a vessels future behavior (position, speed & course), based on events that occur in a predictable pattern, across large map areas. The main concept of this study, is to determine if an ANN is capable of inferring the unique behavioral patterns that each vessel follows and successively use this as a means for predicting multiple vessel behavior into a future point in time. We design, train and implement a proof of concept ANN, as a cloud based web application, with the ability of overlaying predicted short and long term vessel behavior on an interactive map. Our proposed approach could potentially assist in busy port scheduling, vessel route planning, anomaly detection and increasing overall Maritime Domain Awareness.

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